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The Scientific World Journal
Volume 2014 (2014), Article ID 258749, 8 pages
http://dx.doi.org/10.1155/2014/258749
Research Article

An Investigation of Generalized Differential Evolution Metaheuristic for Multiobjective Optimal Crop-Mix Planning Decision

1Department of Information Technology, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa
2Department of Civil Engineering and Surveying, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa

Received 12 February 2014; Accepted 24 March 2014; Published 23 April 2014

Academic Editors: T. O. Ting and X.-S. Yang

Copyright © 2014 Oluwole Adekanmbi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms—being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem.